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Singapore Med J ; 57(6): 301-6, 2016 Jun.
Article in English | MEDLINE | ID: mdl-27353286

ABSTRACT

INTRODUCTION: 72-hour emergency department (ED) reattendance is a widely-used quality indicator for quality of care and patient safety. It is generally assumed that patients who return within 72 hours of ED discharge (72-hour re-attendees) received inadequate treatment or evaluation. The current literature also suggests considerable variation in probable causes of 72-hour ED reattendances internationally. This study aimed to understand the characteristics of these patients at the ED of a Singapore tertiary hospital. METHODS: We conducted a retrospective cohort study on all ED visits between 1 January 2013 and 31 December 2013. 72-hour re-attendees were compared against non-re-attendees based on patient demographics, mode of arrival, patient acuity category status (i.e. P1/P2/P3/P4), seniority ranking of doctor-in-charge and medical diagnoses. Multivariate analysis using the generalised linear model was conducted on variables associated with 72-hour ED re-attendance. RESULTS: Among 104,751 unique patients, 3,065 (2.93%) were in the 72-hour re-attendees group. Multivariate analysis showed that the following risk factors were associated with higher risk of returning within 72 hours: male gender, older age, arrival by ambulance, triaged as P2, diagnoses of heart problems, abdominal pain or viral infection (all p < 0.001), and Chinese ethnicity (p = 0.006). There was no significant difference in the seniority ranking of the doctor-in-charge between both groups (p = 0.419). CONCLUSION: Several patient and event factors were associated with higher risk of being a 72-hour re-attendee. This study forms the basis for hypothesis generation and further studies to explore reasons behind reattendances so that interventions can be developed to target high-risk groups.


Subject(s)
Emergency Medicine/methods , Emergency Service, Hospital/statistics & numerical data , Patient Readmission , Tertiary Care Centers , Abdominal Pain , Adult , Aged , Aged, 80 and over , China , Data Mining , Electronic Health Records , Emergency Medicine/statistics & numerical data , Female , Humans , Male , Middle Aged , Multivariate Analysis , Patient Discharge , Patient Safety , Quality of Health Care , Retrospective Studies , Risk , Singapore , Triage/methods , Young Adult
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